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a0a02c1bc0
Adds an optional focus topic to /compress: `/compress database schema` guides the summariser to preserve information related to the focus topic (60-70% of summary budget) while compressing everything else more aggressively. Inspired by Claude Code's /compact <focus>. Changes: - context_compressor.py: focus_topic parameter on _generate_summary() and compress(); appends FOCUS TOPIC guidance block to the LLM prompt - run_agent.py: focus_topic parameter on _compress_context(), passed through to the compressor - cli.py: _manual_compress() extracts focus topic from command string, preserves existing manual_compression_feedback integration (no regression) - gateway/run.py: _handle_compress_command() extracts focus from event args and passes through — full gateway parity - commands.py: args_hint="[focus topic]" on /compress CommandDef Salvaged from PR #7459 (CLI /compress focus only — /context command deferred). 15 new tests across CLI, compressor, and gateway.
139 lines
4.7 KiB
Python
139 lines
4.7 KiB
Python
"""Tests for focus_topic flowing through the compressor.
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Verifies that _generate_summary and compress accept and use the focus_topic
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parameter correctly. Inspired by Claude Code's /compact <focus>.
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"""
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from unittest.mock import MagicMock, patch
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from agent.context_compressor import ContextCompressor
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def _make_compressor():
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"""Create a ContextCompressor with minimal state for testing."""
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compressor = ContextCompressor.__new__(ContextCompressor)
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compressor.protect_first_n = 2
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compressor.protect_last_n = 5
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compressor.tail_token_budget = 20000
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compressor.context_length = 200000
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compressor.threshold_percent = 0.80
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compressor.threshold_tokens = 160000
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compressor.max_summary_tokens = 10000
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compressor.quiet_mode = True
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compressor.compression_count = 0
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compressor.last_prompt_tokens = 0
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compressor._previous_summary = None
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compressor._summary_failure_cooldown_until = 0.0
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compressor.summary_model = None
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return compressor
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def test_focus_topic_injected_into_summary_prompt():
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"""When focus_topic is provided, the LLM prompt includes focus guidance."""
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compressor = _make_compressor()
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turns = [
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{"role": "user", "content": "Tell me about the database schema"},
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{"role": "assistant", "content": "The schema has tables: users, orders, products."},
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]
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captured_prompt = {}
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def mock_call_llm(**kwargs):
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captured_prompt["messages"] = kwargs["messages"]
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resp = MagicMock()
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resp.choices = [MagicMock()]
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resp.choices[0].message.content = "## Goal\nUnderstand DB schema."
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return resp
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with patch("agent.context_compressor.call_llm", mock_call_llm):
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result = compressor._generate_summary(turns, focus_topic="database schema")
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assert result is not None
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prompt_text = captured_prompt["messages"][0]["content"]
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assert 'FOCUS TOPIC: "database schema"' in prompt_text
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assert "PRIORITISE" in prompt_text
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assert "60-70%" in prompt_text
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def test_no_focus_topic_no_injection():
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"""Without focus_topic, the prompt doesn't contain focus guidance."""
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compressor = _make_compressor()
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turns = [
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{"role": "user", "content": "Hello"},
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{"role": "assistant", "content": "Hi"},
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]
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captured_prompt = {}
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def mock_call_llm(**kwargs):
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captured_prompt["messages"] = kwargs["messages"]
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resp = MagicMock()
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resp.choices = [MagicMock()]
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resp.choices[0].message.content = "## Goal\nGreeting."
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return resp
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with patch("agent.context_compressor.call_llm", mock_call_llm):
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result = compressor._generate_summary(turns)
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prompt_text = captured_prompt["messages"][0]["content"]
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assert "FOCUS TOPIC" not in prompt_text
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def test_compress_passes_focus_to_generate_summary():
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"""compress() passes focus_topic through to _generate_summary."""
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compressor = _make_compressor()
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# Track what _generate_summary receives
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received_kwargs = {}
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original_generate = compressor._generate_summary
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def tracking_generate(turns, **kwargs):
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received_kwargs.update(kwargs)
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return "## Goal\nTest."
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compressor._generate_summary = tracking_generate
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messages = [
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{"role": "system", "content": "System prompt"},
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{"role": "user", "content": "first"},
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{"role": "assistant", "content": "reply1"},
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{"role": "user", "content": "second"},
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{"role": "assistant", "content": "reply2"},
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{"role": "user", "content": "third"},
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{"role": "assistant", "content": "reply3"},
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{"role": "user", "content": "fourth"},
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{"role": "assistant", "content": "reply4"},
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]
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compressor.compress(messages, current_tokens=100000, focus_topic="authentication flow")
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assert received_kwargs.get("focus_topic") == "authentication flow"
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def test_compress_none_focus_by_default():
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"""compress() passes None focus_topic by default."""
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compressor = _make_compressor()
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received_kwargs = {}
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def tracking_generate(turns, **kwargs):
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received_kwargs.update(kwargs)
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return "## Goal\nTest."
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compressor._generate_summary = tracking_generate
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messages = [
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{"role": "system", "content": "System prompt"},
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{"role": "user", "content": "first"},
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{"role": "assistant", "content": "reply1"},
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{"role": "user", "content": "second"},
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{"role": "assistant", "content": "reply2"},
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{"role": "user", "content": "third"},
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{"role": "assistant", "content": "reply3"},
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{"role": "user", "content": "fourth"},
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{"role": "assistant", "content": "reply4"},
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]
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compressor.compress(messages, current_tokens=100000)
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assert received_kwargs.get("focus_topic") is None
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